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Sur les effets du changement climatique aux échelles globale et régionale. Il montre en particulierqu’aucun consensus ne peut être trouvé pour ce qui concerne l’évolution future de lapluviométrie — et de la dynamique atmosphérique associée — en région de mousson africaine.Ce mémoire revisite cette question à la lumière des nouvelles données disponibles et selon uneapproche évitant toute surreprésentation du nombre de simulations disponibles pour un type demodèle donné, tout en prenant en compte la diversité des modèles ainsi que leur évolution dansle temps : sorties de vingt modèles de circulation générale (MCGs) ayant participé aux exercicesCMIP3 (douze MCGs) et CMIP5 (huit MCGs) sous les scénarios d’émissions A1B et rcp4.5,respectivement. Les sorties sont analysées principalement sur deux fenêtres de quarante ans —périodes actuelle (1960-1999) et future (2031-2070) — et les résultats discutés au regard de leurvraisemblance selon une approche permettant à la fois de quantifier les différences futur moinsactuel, de mesurer les significativités et les robustesses statistiques et d’associer une probabilitémesurant le consensus des modèles en fonction des échelles et des variables considérées.Les analyses menées sur CMIP3 et CMIP5 montrent qu’un consensus sur l’effet du changementclimatique en Afrique de l’Ouest peut être obtenu si l’on ne fait pas de l’ensemble de labande sahélienne une entité homogène et qu’on raisonne à des échelles spatiales inférieures. Lesrésultats révèlent une évolution contrastée entre le centre et l’ouest du Sahel avec, pour le futur(i) une hausse des précipitations au centre s’expliquant surtout par une plus grande convergencedes flux dans les basses couches, ainsi qu’une pénétration plus au nord de la mousson ;(ii) une baisse des précipitations à l’ouest s’expliquant par le renforcement de la circulation detype Walker, du Jet d’Est Africain (JEA) et de la subsidence dans les couches moyennes. Parailleurs, on peut s’attendre à une modification du cycle annuel moyen avec un retrait retardé dela mousson. Ce retard est notamment lié aux apports supplémentaires d’humidité depuis l’Atlantique,dus au renforcement des contrastes thermiques et d’humidité entre océan et continent,mais aussi et surtout aux apports tardifs d’humidité depuis la Méditerranée et au renforcementdes flux de nord en septembre et octobre en direction du Sahel / The fourth IPCC report in 2007 established the synthesis of previously published work onthe effects of climate change on global and regional scales. It shows in particular that no consensuscan be found with regard to the future of rainfall — and atmospheric dynamics- associatedwith region — African monsoon. This dissertation revisits this issue in the light of new dataand using an approach avoiding over-representation of the number of simulations available forone type of model and taking into account the diversity of models and their evolution in time :twenty general circulation models (GCMs) participating in the exercises CMIP3 (twelve GCMs)and CMIP5 (eight GCMs) under the A1B emissions scenario and rcp4.5, respectively. Outputsare analyzed on two 40-year periods, — ‘Present’ (1960-1999) and ‘Future’ (2031-2070) — anddiscussed in terms of likelihood, through an approach allowing us to both quantify differences‘future’ minus ‘present’, measure robustness and statistical significances and associate a probabilitymeasuring the model consensus as a function of scales and variables.Analyzes conducted on CMIP3 and CMIP5 show that consensus on the effect of climatechange in West Africa can be achieved if we do not consider the Sahel as a whole and homogeneousentity but at lower scales. The results show contrasted responses over the centraland western Sahel, with for the future, (i) an increase in precipitation in the central regionexplained primarily by a greater convergence of flow in the lower layers and a most northerlymonsoon penetration over the continent, (ii) a rainfall decrease in the western Sahel explainedby increased Walker-type circulation, African easterly jet and mid-level subsidence. Moreover,we can expect a change in the mean annual cycle of the monsoon season with a delayed withdrawallinked to additional inputs of moisture from the Atlantic due to increasing thermal andmoisture contrasts between ocean and continent but also to a stronger contribution of moisturefluxes in September and October from the Mediterranean into the Sahel

The atmospheres of exoplanets are being characterised in increasing detail by observational facilities and will be examined with even greater clarity with upcoming space based missions such as the James Webb Space Telescope (JWST) and the Wide Field InfraRed Survey Telescope (WFIRST). A major component of exoplanet atmospheres is the presence of cloud particles which produce characteristic observational signatures in transit spectra and influence the geometric albedo of exoplanets. Despite a decade of observational evidence, the formation, dynamics and radiative-transport of exoplanet atmospheric cloud particles remains an open question in the exoplanet community. In this thesis, we investigate the kinetic chemistry of cloud formation in hot Jupiter exoplanets, their effect on the atmospheric dynamics and observable properties. We use a static 1D cloud formation code to investigate the cloud formation properties of the hot Jupiter HD 189733b. We couple a time-dependent kinetic cloud formation to a 3D radiative-hydrodynamic simulation of the atmosphere of HD 189733b and investigate the dynamical properties of cloud particles in the atmosphere. We develop a 3D multiple-scattering Monte Carlo radiative-transfer code to post-process the results of the cloudy HD 189733b RHD simulation and compare the results to observational results. We find that the cloud structures of the hot Jupiter HD 189733b are likely to be highly inhomogeneous, with differences in cloud particle sizes, number density and composition with longitude, latitude and depth. Cloud structures are most divergent between the dayside and nightside faces of the planet due to the instability of silicate materials on the hotter dayside. We find that the HD 189733b simulation in post-processing is consistent with geometric albedo observations of the planet. Due to the scattering properties of the cloud particles we predict that HD 189733b will be brighter in the upcoming space missions CHaracterising ExOPlanet Satellite (CHEOPS) bandpass compared to the Transiting Exoplanet Space Survey (TESS) bandpass.

General Circulation Models (GCMs), which are mathematical models based on principles of fluid dynamics, thermodynamics and radiative transfer, are the most reliable tools available for projecting climate change. However, the spatial scale on which typical GCMs operate is very coarse as compared to that of a hydrologic process and hence, the output from a GCM cannot be directly used in hydrologic models. Statistical Downscaling (SD) derives a statistical or empirical relationship between the variables simulated by the GCM (predictors) and a point-scale meteorological series (predictand). In this work, a new downscaling model called CRF-downscaling model, is developed where the conditional distribution of the hydrologic predictand sequence, given atmospheric predictor variables, is represented as a conditional random field (CRF) to downscale the predictand in a probabilistic framework. Features defined in the downscaling model capture information about various factors influencing precipitation such as circulation patterns, temperature and pressure gradients and specific humidity levels. Uncertainty in prediction is addressed by projecting future cumulative distribution functions (CDFs) for a number of most likely precipitation sequences. Direct classification of dry/wet days as well as precipitation amount is achieved within a single modeling framework, and changes in the non-parametric distribution of precipitation and dry and wet spell lengths are projected. Application of the method is demonstrated with the case study of downscaling to daily precipitation in the Mahanadi basin in Orissa, with the A1B scenario of the MIROC3.2 GCM from the Center for Climate System Research (CCSR), Japan. An uncertainty modeling framework is presented in this work, which combines GCM, scenario and downscaling uncertainty using the Dempster-Shafer (D-S) evidence theory for representing and combining uncertainty. The methodology for combining uncertainties is applied to projections of hydrologic drought in terms of monsoon standardized streamflow index (SSFI-4) from streamflow projections for the Mahanadi river at Hirakud. The results from the work indicate an increasing probability of extreme, severe and moderate drought and decreasing probability of normal to wet conditions, as a result of a decrease in monsoon streamflow in the Mahanadi river due to climate change. In most studies to date, the nature of the downscaling relationship is assumed stationary, or remaining unchanged in a future climate. In this work, an uncertainty modeling framework is presented in which, in addition to GCM and scenario uncertainty, uncertainty in the downscaling relationship itself is explored by linking downscaling with changes in frequencies of modes of natural variability. Downscaling relationships are derived for each natural variability cluster and used for projections of hydrologic drought. Each projection is weighted with the future projected frequency of occurrence of that cluster, called ‘cluster-linking’, and scaled by the GCM performance with respect to the associated cluster for the present period, called ‘frequency scaling’. The uncertainty modeling framework is applied to a case study of projections of hydrologic drought or SSFI-4 classifications, using projected streamflows for the Mahanadi river at Hirakud. It is shown that a stationary downscaling relationship will either over- or under-predict downscaled hydrologic variable values and associated uncertainty. Results from the work show improved agreement between GCM predictions at the regional scale, which are validated for the 20th century, implying that frequency scaling and cluster-linking may indeed be a valid method for constraining uncertainty. To assess the impact of climate change on reservoir performance, in this study, a range of integrated hydrologic scenarios are projected for the future. The hydrologic scenarios incorporate increased irrigation demands; rule curves dictated by increased need for flood storage and downscaled projections of streamflow from an ensemble of GCMs and emission scenarios. The impact of climate change on multipurpose reservoir performance is quantified, using annual hydropower and RRV criteria, under GCM and scenario uncertainty. The ‘business-as-usual’ case using Standard Operating Policy (SOP) is studied initially for quantifying impacts. Adaptive Stochastic Dynamic Programming (SDP) policies are subsequently derived for the range of future hydrologic scenarios, with the objective of maximizing reliabilities with respect to multiple reservoir purposes of hydropower, irrigation and flood control. It is shown that the hydrologic impact of climate change is likely to result in decreases in performance criteria and annual hydropower generation for Hirakud reservoir. Adaptive policies show that a marginal reduction in irrigation and flood control reliability can achieve increased hydropower reliability in future. Hence, reservoir rules for flood control may have to be revised in the future.

General Circulation Models (GCMs) are tools designed to simulate time series of climate variables globally, accounting for effects of greenhouse gases in the atmosphere. They attempt to represent the physical processes in the atmosphere, ocean, cryosphere and land surface. They are currently the most credible tools available for simulating the response of the global climate system to increasing greenhouse gas concentrations, and to provide estimates of climate variables (e.g. air temperature, precipitation, wind speed, pressure etc.) on a global scale. GCMs demonstrate a significant skill at the continental and hemispheric spatial scales and incorporate a large proportion of the complexity of the global system; they are, however, inherently unable to represent local subgrid-scale features and dynamics. The spatial scale on which a GCM can operate (e.g., 3.75° longitude x 3.75° latitude for Coupled Global Climate Model, CGCM2) is very coarse compared to that of a hydrologic process (e.g., precipitation in a region, streamflow in a river etc.) of interest in the climate change impact assessment studies. Moreover, accuracy of GCMs, in general, decreases from climate related variables, such as wind, temperature, humidity and air pressure to hydrologic variables such as precipitation, evapotranspiration, runoff and soil moisture, which are also simulated by GCMs. These limitations of the GCMs restrict the direct use of their output in hydrology. This thesis deals with developing statistical downscaling models to assess climate change impacts and methodologies to address GCM and scenario uncertainties in assessing climate change impacts on hydrology. Downscaling, in the context of hydrology, is a method to project the hydrologic variables (e.g., rainfall and streamflow) at a smaller scale based on large scale climatological variables (e.g., mean sea level pressure) simulated by a GCM. A statistical downscaling model is first developed in the thesis to predict the rainfall over Orissa meteorological subdivision from GCM output of large scale Mean Sea Level Pressure (MSLP). Gridded monthly MSLP data for the period 1948 to 2002, are obtained from the National Center for Environmental Prediction/ National Center for Atmospheric Research (NCEP/NCAR) reanalysis project for a region spanning 150 N -250 N in latitude and 800 E -900 E in longitude that encapsulates the study region. The downscaling model comprises of Principal Component Analysis (PCA), Fuzzy Clustering and Linear Regression. PCA is carried out to reduce the dimensionality of the larger scale MSLP and also to convert the correlated variables to uncorrelated variables. Fuzzy clustering is performed to derive the membership of the principal components in each of the clusters and the memberships obtained are used in regression to statistically relate MSLP and rainfall. The statistical relationship thus obtained is used to predict the rainfall from GCM output. The rainfall predicted with the GCM developed by CCSR/NIES with B2 scenario presents a decreasing trend for non-monsoon period, for the case study. Climate change impact assessment models developed based on downscaled GCM output are subjected to a range of uncertainties due to both ‘incomplete knowledge’ and ‘unknowable future scenario’ (New and Hulme, 2000). ‘Incomplete knowledge’ mainly arises from inadequate information and understanding about the underlying geophysical process of global change, leading to limitations in the accuracy of GCMs. This is also termed as GCM uncertainty. Uncertainty due to ‘unknowable future scenario’ is associated with the unpredictability in the forecast of socio-economic and human behavior resulting in future Green House Gas (GHG) emission scenarios, and can also be termed as scenario uncertainty. Downscaled outputs of a single GCM with a single climate change scenario represent a single trajectory among a number of realizations derived using various GCMs and scenarios. Such a single trajectory alone can not represent a future hydrologic scenario, and will not be useful in assessing hydrologic impacts due to climate change. Nonparametric methods are developed in the thesis to model GCM and scenario uncertainty for prediction of drought scenario with Orissa meteorological subdivision as a case study. Using the downscaling technique described in the previous paragraph, future rainfall scenarios are obtained for all available GCMs and scenarios. After correcting for bias, equiprobability transformation is used to convert the precipitation into Standardized Precipitation Index-12 (SPI-12), an annual drought indicator, based on which a drought may be classified as a severe drought, mild drought etc. Disagreements are observed between different predictions of SPI-12, resulting from different GCMs and scenarios. Assuming SPI-12 to be a random variable at every time step, nonparametric methods based on kernel density estimation and orthonormal series are used to determine the nonparametric probability density function (pdf) of SPI-12. Probabilities for different categories of drought are computed from the estimated pdf. It is observed that there is an increasing trend in the probability of extreme drought and a decreasing trend in the probability of near normal conditions, in the Orissa meteorological subdivision. The single valued Cumulative Distribution Functions (CDFs) obtained from nonparametric methods suffer from limitations due to the following: (a) simulations for all scenarios are not available for all the GCMs, thus leading to a possibility that incorporation of these missing climate experiments may result in a different CDF, (b) the method may simply overfit to a multimodal distribution from a relatively small sample of GCMs with a limited number of scenarios, and (c) the set of all scenarios may not fully compose the universal sample space, and thus, the precise single valued probability distribution may not be representative enough for applications. To overcome these limitations, an interval regression is performed to fit an imprecise normal distribution to the SPI-12 to provide a band of CDFs instead of a single valued CDF. Such a band of CDFs represents the incomplete nature of knowledge, thus reflecting the extent of what is ignored in the climate change impact assessment. From imprecise CDFs, the imprecise probabilities of different categories of drought are computed. These results also show an increasing trend of the bounds of the probability of extreme drought and decreasing trend of the bounds of the probability of near normal conditions, in the Orissa meteorological subdivision. Water resources planning requires the information about future streamflow scenarios in a river basin to combat hydrologic extremes resulting from climate change. It is therefore necessary to downscale GCM projections for streamflow prediction at river basin scales. A statistical downscaling model based on PCA, fuzzy clustering and Relevance Vector Machine (RVM) is developed to predict the monsoon streamflow of Mahanadi river at Hirakud reservoir, from GCM projections of large scale climatological data. Surface air temperature at 2m, Mean Sea Level Pressure (MSLP), geopotential height at a pressure level of 500 hecto Pascal (hPa) and surface specific humidity are considered as the predictors for modeling Mahanadi streamflow in monsoon season. PCA is used to reduce the dimensionality of the predictor dataset and also to convert the correlated variables to uncorrelated variables. Fuzzy clustering is carried out to derive the membership of the principal components in each of the clusters and the memberships thus obtained are used in RVM regression model. RVM involves fewer number of relevant vectors and the chance of overfitting is less than that of Support Vector Machine (SVM). Different kernel functions are used for comparison purpose and it is concluded that heavy tailed Radial Basis Function (RBF) performs best for streamflow prediction with GCM output for the case considered. The GCM CCSR/NIES with B2 scenario projects a decreasing trend in future monsoon streamflow of Mahanadi which is likely to be due to high surface warming. A possibilistic approach is developed next, for modeling GCM and scenario uncertainty in projection of monsoon streamflow of Mahanadi river. Three GCMs, Center for Climate System Research/ National Institute for Environmental Studies (CCSR/NIES), Hadley Climate Model 3 (HadCM3) and Coupled Global Climate Model 2 (CGCM2) with two scenarios A2 and B2 are used for the purpose. Possibilities are assigned to GCMs and scenarios based on their system performance measure in predicting the streamflow during years 1991-2005, when signals of climate forcing are visible. The possibilities are used as weights for deriving the possibilistic mean CDF for the three standard time slices, 2020s, 2050s and 2080s. It is observed that the value of streamflow at which the possibilistic mean CDF reaches the value of 1 reduces with time, which shows reduction in probability of occurrence of extreme high flow events in future and therefore there is likely to be a decreasing trend in the monthly peak flow. One possible reason for such a decreasing trend may be the significant increase in temperature due to climate warming. Simultaneous occurrence of reduction in Mahandai streamflow and increase in extreme drought in Orissa meteorological subdivision is likely to pose a challenge for water resources engineers in meeting water demands in future.

Understanding climate change and variability at urban scale is essential for water resource management, land use planning, and development of adaption plans. However, there are serious challenges to meet these goals due to unavailability of observed and / or simulated high resolution spatial and temporal climate data. Recent efforts made possible the availability of high resolution climate data from non-hydrostatic regional climate model (RCM) and statistically downscaled General Circulation Models (GCMs). This study investigates trends in climate and urbanization and their impact on surface water supply for the city of Addis Ababa, Ethiopia. The methodology presented in this study focused on the observed and projected NIMRHadGEM2- AO model and Special Report on Emissions Scenarios (SRES) of B2 and A2 of HadCM3 model are also employed for rainfall, maximum temperature and minimum temperature data using for climate analysis. Water Evaluation and Planning (WEAP) modeling system was used for determination of climate and urbanization impacts on water. Land-Sat images were analyzed using Normalized Differencing Vegetation Index (NDVI). Statistical downscaling model (SDSM) was employed to investigate the major changes and intensity of the urban heat island (UHI). The result indicates monthly rainfall anomalies with respect to the baseline mean showing wet anomaly in summer (kiremt) during 2030s and 2050s, and a dry anomaly in the 2080s under A2 and B2 scenarios with exception of a wet anomaly in September over the city. The maximum temperature anomalies under Representative Concentration Pathways (RCPs) also show warming during near, mid and end terms. The mean monthly minimum temperature anomalies under A2 and B2 scenarios are warm but the anomalies are much lower than RCPs. The climate under the RCP 8.5 and high population growth (3.3 %) scenario will lead to the unmet demand of 462.77 million m3 by 2039. Future projection of urban heat island under emission pathway of A2 and B2 scenario shows that, the nocturnal UHI will be intense in winter or dry season episodes in the city. Under A2 scenario the highest urban warming will occur during October to December (2.5 ºC to 3.2 ºC). Under RCP 8.5 scenario the highest urban warming will occur during October to December (0.5 ºC to 1.0 °C) in the 2050s and 2080s. Future management and adaptation strategies are to expand water supply to meet future demand and to implement demand side water management systems of the city and UHI / Environmental Sciences / Ph. D. (Environmental Management)

Submitted by Marco Antonio Oliveira da Silva (maosilva@ipen.br) on 2016-08-26T12:15:03Z No. of bitstreams: 0 / Made available in DSpace on 2016-08-26T12:15:03Z (GMT). No. of bitstreams: 0 / O escoamento bifásico de gás-líquido é encontrado em muitos circuitos fechados que utilizam circulação natural para fins de resfriamento. O fenômeno da circulação natural é importante nos recentes projetos de centrais nucleares para a remoção de calor. O circuito de circulação natural (Circuito de Circulação Natural - CCN), instalado no Instituto de Pesquisas Energéticas e Nucleares, IPEN / CNEN, é um circuito experimento concebido para fornecer dados termo-hidráulicos relacionados com escoamento monofásico ou bifásico em condições de circulação natural. A estimativa de transferência de calor tem sido melhorada com base em modelos que requerem uma previsão precisa de transições de padrão de escoamento. Este trabalho apresenta testes experimentais desenvolvidos no CCN para a visualização dos fenômenos de instabilidade em ciclos de circulação natural básica e classificar os padrões de escoamento bifásico associados aos transientes e instabilidades estáticas de escoamento. As imagens são comparadas e agrupadas utilizando mapas auto-organizáveis de Kohonen (SOM), aplicados em diferentes características da imagem digital. Coeficientes da Transformada Discreta de Cossenos de Quadro Completo (FFDCT) foram utilizados como entrada para a tarefa de classificação, levando a bons resultados. Os protótipos de FFDCT obtidos podem ser associados a cada padrão de escoamento possibilitando uma melhor compreensão da instabilidade observada. Uma metodologia sistemática foi utilizada para verificar a robustez do método. / Dissertação (Mestrado em Tecnologia Nuclear) / IPEN/D / Instituto de Pesquisas Energéticas e Nucleares - IPEN-CNEN/SP

Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2016-05-31T09:35:51Z No. of bitstreams: 2 Tese - Frederico Augusto Martins Valtuille Faleiro - 2016.pdf: 7096330 bytes, checksum: 04cfce04ef128c5bd6e99ce18bb7f650 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-05-31T10:52:51Z (GMT) No. of bitstreams: 2 Tese - Frederico Augusto Martins Valtuille Faleiro - 2016.pdf: 7096330 bytes, checksum: 04cfce04ef128c5bd6e99ce18bb7f650 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2016-05-31T10:52:51Z (GMT). No. of bitstreams: 2 Tese - Frederico Augusto Martins Valtuille Faleiro - 2016.pdf: 7096330 bytes, checksum: 04cfce04ef128c5bd6e99ce18bb7f650 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2016-03-07 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The climate changes are one of the major threats to the biodiversity and it is expected to increase its impact along the 21st century. The climate change affect all levels of the biodiversity from individuals to biomes, reducing the ecosystem services. Despite of this, the prediction of climate change impacts on biodiversity is still a challenge. Overcoming these issues depends on improvements in different aspects of science that support predictions of climate change impact on biodiversity. The common practice to predict the climate change impact consists in formulate ecological niche models based in the current climate and project the changes based in the future climate predicted by the climate models. However, there are some recognized limitations both in the formulation of the ecological niche model and in the use of predictions from the climate models that need to be analyzed. Here, in the first chapter we review the science behind the climate models in order to reduce the knowledge gap between the scientific community that formulate the climate models and the community that use the predictions of these models. We showed that there is not consensus about evaluate the climate models, obtain regional models with higher spatial resolution and define consensual models. However, we gave some guidelines for use the predictions of the climate models. In the second chapter, we tested if the predictions of correlative ecological niche models fitted with presence-absence match the predictions of models fitted with abundance data on the metrics of climate change impact on orchid bees in the Atlantic Forest. We found that the presence-absence models were a partial proxy of change in abundance when the output of the models was continuous, but the same was not true when the predictions were converted to binary. The orchid bees in general will decrease the abundance in the future, but will retain a good amount of suitable sites in the future and the distance to gained climatic suitable areas can be very close, despite of great variation. The change in the species richness and turnover will be mainly in the western and some regions of southern of the Atlantic Forest. In the third chapter, we discussed the drawbacks in using the estimations of realized niche instead the fundamental niche, such as overpredicting the effect of climate change on species’ extinction risk. We proposed a framework based on phylogenetic comparative and missing data methods to predict the dimensions of the fundamental niche of species with missing data. Moreover, we explore sources of uncertainty in predictions of fundamental niche and highlight future directions to overcome current limitations of phylogenetic comparative and missing data methods to improve predictions. We conclude that it is possible to make better use of the current knowledge about species’ fundamental niche with phylogenetic information and auxiliary traits to predict the fundamental niche of poorly-studied species. In the fourth chapter, we used the framework of the chapter three to test the performance of two recent phylogenetic modeling methods to predict the thermal niche of mammals. We showed that PhyloPars had better performance than Phylogenetic Eigenvector Maps in predict the thermal niche. Moreover, the error and bias had similar phylogenetic pattern for both margins of the thermal niche while they had differences in the geographic pattern. The variance in the performance was explained by taxonomic differences and not by methodological aspects. Finally, our models better predicted the upper margin than the lower margin of the thermal niche. This is a good news for predicting the effect of climate change on species without physiological data. We hope our finds can be used to improve the predictions of climate change effect on the biodiversity in future studies and support the political decisions on minimizing the effects of climate change on biodiversity. / As mudanças climáticas são uma das principais ameaças à biodiversidade e é esperado que aumente seu impacto ao longo do século XXI. As mudanças climáticas afetam todos os níveis de biodiversidade, de indivíduos à biomas, reduzindo os serviços ecossistêmicos. Apesar disso, as predições dos impactos das mudanças climáticas na biodiversidade é ainda um desafio. A superação dessas questões depende de melhorias em diferentes aspectos da ciência que dá suporte para predizer o impacto das mudanças climáticas na biodiversidade. A prática comum para predizer o impacto das mudanças climáticas consiste em formular modelos de nicho ecológico baseado no clima atual e projetar as mudanças baseadas no clima futuro predito pelos modelos climáticos. No entanto, existem algumas limitações reconhecidas na formulação do modelo de nicho ecológico e no uso das predições dos modelos climáticos que precisam ser analisadas. Aqui, no primeiro capítulo nós revisamos a ciência por detrás dos modelos climáticos com o intuito de reduzir a lacuna de conhecimentos entre a comunidade científica que formula os modelos climáticos e a comunidade que usa as predições dos modelos. Nós mostramos que não existe consenso sobre avaliar os modelos climáticos, obter modelos regionais com maior resolução espacial e definir modelos consensuais. No entanto, nós damos algumas orientações para usar as predições dos modelos climáticos. No segundo capítulo, nós testamos se as predições dos modelos correlativos de nicho ecológicos ajustados com presença-ausência são congruentes com aqueles ajustados com dados de abundância nas medidas de impacto das mudanças climáticas em abelhas de orquídeas da Mata Atlântica. Nós encontramos que os modelos com presença-ausência foram substitutos parciais das mudanças na abundância quando o resultado dos modelos foi contínuo (adequabilidade), mas o mesmo não ocorreu quando as predições foram convertidas para binárias. As espécies de abelhas, de modo geral, irão diminuir em abundância no futuro, mas reterão uma boa quantidade de locais adequados no futuro e a distância para áreas climáticas adequadas ganhadas podem estar bem próximo, apesar da grande variação. A mudança na riqueza e na substituição de espécies ocorrerá principalmente no Oeste e algumas regiões no sul da Mata Atlântica. No terceiro capítulo, nós discutimos as desvantagens no uso de estimativas do nicho realizado ao invés do nicho fundamental, como superestimar o efeito das mudanças climáticas no risco de extinção das espécies. Nós propomos um esquema geral baseado em métodos filogenéticos comparativos e métodos de dados faltantes para predizer as dimensões do nicho fundamental das espécies com dados faltantes. Além disso, nós exploramos as fontes de incerteza nas predições do nicho fundamental e destacamos direções futuras para superar as limitações atuais dos métodos comparativos filogenéticas e métodos de dados faltantes para melhorar as predições. Nós concluímos que é possível fazer melhor uso do conhecimento atual sobre o nicho fundamental das espécies com informação filogenética e caracteres auxiliares para predizer o nicho fundamental de espécies pouco estudadas. No quarto capítulo, nós usamos o esquema geral do capítulo três para testar a performance de dois novos métodos de modelagem filogenética para predizer o nicho térmico dos mamíferos. Nós mostramos que o “PhyloPars” teve uma melhor performance que o “Phylogenetic Eigenvector Maps” em predizer o nicho térmico. Além disso, o erro e o viés tiveram um padrão filogenético similar para ambas as margens do nicho térmico, enquanto eles apresentaram diferentes padrões espaciais. A variância na performance foi explicada pelas diferenças taxonômicas e não pelas diferenças em aspectos metodológicos. Finalmente, nossos modelos melhor predizem a margem superior do que a margem inferior do nicho térmico. Essa é uma boa notícia para predizer o efeito das mudanças climáticas em espécies sem dados fisiológicos. Nós esperamos que nossos resultados possam ser usados para melhorar as predições do efeito das mudanças climáticas na biodiversidade em estudos futuros e dar suporte para decisões políticas para minimização dos efeitos das mudanças climáticas na biodiversidade.

En Amérique latine le XIXe siècle se caractérise par l'indépendance des anciennes colonies espagnoles, par le processus de formation des État-Nation et par la reconstruction des ordres juridiques des nouveaux États. Une fois le modèle républicain adopté, il fallait créer des institutions juridiques nouvelles et un système normatif qui régule le destin des nouvelles sociétés en concordance avec la réalité locale et, surtout, le plus différent possible de l'ancien régime colonial. Dans toutes ces transformations les hommes de loi (avocats, juges, juristes, bureaucrates) ont joué un rôle important. En tant que propriétaires du savoir juridique et politique ils étaient les personnages idoines, pour effectuer la tâche d'organisation de l'État. Parmi ces tâches, la structuration de la justice était essentielle pour les nouveaux États. Montrer l'implication du juge dans ce processus de consolidation, est une des finalités de ce travail. D'autre part, la construction de l'imaginaire juridique latino-américain s'est réalisée sous l'influence des modèles juridiques étrangers, dont la France, l'Angleterre, les États-Unis. Dans un classicisme juridique particulier, la réception et la transformation de ces modèles a créé une conscience juridique locale originale et très différente du modèle initial en combinant l'exégèse française avec le conceptualisme allemand / The nineteenth century in Latin America is characterized by the independence of the former Spanish colonies, the formation process of nation-state and the reconstruction of legal systems of the new states. Once the republican model adopted, it was necessary to create new legal institutions and a legal system to regulate the destiny of new societies according to the local reality and, above all, as different as possible from the old colonial regime. In all these transformations the jurists (lawyers, judges, jurists, bureaucrats) have played an important role. As owners of legal and politic knowledge, they were the ones suitable to organize the state. Among this organization task, structuring the justice was essential for the new states. To show the implication of judges in this consolidation process is one of the purposes of this work. Besides, the construction of Latin American legal imaginary was carried out under the influence of foreign legal models, like France, Britain and the United States. In a special legal classicism, the reception and transformation of these models has created a proper legal consciousness, innovative and very different from the original model, combining French exegesis with German conceptualism / En América Latina, el siglo XIX se caracteriza por la independencia de las antiguas colonias españolas, el proceso de formación de los Estados-Nación y la reconstrucción de los ordenamientos jurídicos de los nuevos Estados. Una vez adoptado el modelo republicano, fue necesario la creación de instituciones jurídicas nuevas y un sistema normativo que regulara el destino de las nuevas sociedades conforme a la realidad local y, sobre todo, lo más distante posible del antiguo régimen colonial. En todas estas transformaciones, los hombres de ley (abogados, jueces, juristas y burócratas) tuvieron un papel importante ; como proprietarios del saber jurídico y político, fueron los personajes idóneos para realizar la tarea de organización del Estado. Dentro de esa tarea, la estructuración de la justicia fue fundamental para los nuevos Estados. Una de las finalidades de este trabajo es demostrar la implicación de los jueces en ese proceso de consolidación. Además, la construcción del imaginario jurídico latinoamericano se llevó a cabo bajo la influencia de modelos jurídicos extranjeros dentro de los cuales se destacan Francia, Alemania y Estados Unidos. La recepción y tranformación de dichos modelos dentro de un clasicismo jurídico particular, que combinaba la exégesis francesa y el conceptualismo alemán, generó una conciencia jurídica local original y muy diferente del modelo inicial